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Unsupervised domain adaptation (UDA) aims to enhance the generalization capability of a certain model from a source domain to a target domain.
Imagenet: A large-scale hierarchical image database
J. Deng, W. Dong, R. Socher, L.-J. Li, K. Li, and L. Fei-Fei · 2009
Earlier work this paper cites.
The pascal visual object classes (voc) challenge
M. Everingham, L. Van Gool, C. K. Williams, J. Winn, and A. Zisserman · 2010
Earlier work this paper cites.
Co-training for domain adaptation
M. Chen, K. Q. Weinberger, and J. Blitzer · 2011
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Imagenet classification with deep convolutional neural networks
A. Krizhevsky, I. Sutskever, and G. E. Hinton · 2012
Earlier work this paper cites.
Microsoft coco: Common objects in context
T.-Y. Lin, M. Maire, S. Belongie, J. Hays, P. Perona, D. Ramanan, P. Dollár, and C. L. Zitnick · 2014
Earlier work this paper cites.
Virtual and real world adaptation for pedestrian detection
D. Vazquez, A. M. Lopez, J. Marin, D. Ponsa, and D. Geronimo · 2014
Earlier work this paper cites.
Fully convolutional networks for semantic segmentation
J. Long, E. Shelhamer, and T. Darrell · 2015
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Learning transferable features with deep adaptation networks
M. Long, Y. Cao, J. Wang, and M. Jordan · 2015
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Faster r-cnn: Towards real-time object detection with region proposal networks
S. Ren, K. He, R. Girshick, and J. Sun · 2015
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Very deep convolutional networks for large-scale image recognition
K. Simonyan and A. Zisserman · 2015
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The cityscapes dataset for semantic urban scene understanding
M. Cordts, M. Omran, S. Ramos, T. Rehfeld, M. Enzweiler, R. Benenson, U. Franke, S. Roth, and B. Schiele · 2016
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Deep residual learning for image recognition
K. He, X. Zhang, S. Ren, and J. Sun · 2016
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Fcns in the wild: Pixel-level adversarial and constraint-based adaptation
J. Hoffman, D. Wang, F. Yu, and T. Darrell · 2016
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Coupled generative adversarial networks
M.-Y. Liu and O. Tuzel · 2016
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Joint intermodal and intramodal label transfers for extremely rare or unseen classes
G.-J. Qi, W. Liu, C. Aggarwal, and T. Huang · 2016
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Playing for data: Ground truth from computer games
S. R. Richter, V. Vineet, S. Roth, and V. Koltun · 2016
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The synthia dataset: A large collection of synthetic images for semantic segmentation of urban scenes
G. Ros, L. Sellart, J. Materzynska, D. Vazquez, and A. M. Lopez · 2016
Cited alongside, same era.
Unsupervised pixel-level domain adaptation with generative adversarial networks
K. Bousmalis, N. Silberman, D. Dohan, D. Erhan, and D. Krishnan · 2017
Cited alongside, same era.
Deeplab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs
L.-C. Chen, G. Papandreou, I. Kokkinos, K. Murphy, and A. L. Yuille · 2017
Cited alongside, same era.
No more discrimination: Cross city adaptation of road scene segmenters
Y.-H. Chen, W.-Y. Chen, Y.-T. Chen, B.-C. Tsai, Y.-C. Frank Wang, and M. Sun · 2017
Cited alongside, same era.
Mask r-cnn
K. He, G. Gkioxari, P. Dollár, and R. Girshick · 2017
Cited alongside, same era.
Adversarial discriminative domain adaptation
Y. Luo, L. Zheng, T. Guan, J. Yu, and Y. Yang · 2018
Later among the works it cites.
Image to image translation for domain adaptation
Z. Murez, S. Kolouri, D. Kriegman, R. Ramamoorthi, and K. Kim · 2018
Later among the works it cites.
Unsupervised domain adaptation with similarity learning
P. O. Pinheiro · 2018
Later among the works it cites.
Maximum classifier discrepancy for unsupervised domain adaptation
K. Saito, K. Watanabe, Y. Ushiku, and T. Harada · 2018
Later among the works it cites.
Learning from synthetic data: Addressing domain shift for semantic segmentation
S. Sankaranarayanan, Y. Balaji, A. Jain, S. Nam Lim, and R. Chellappa · 2018
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Learning to adapt structured output space for semantic segmentation
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E. Tzeng, J. Hoffman, K. Saenko, and T. Darrell · 2017
Cited alongside, same era.
Fast haze removal for nighttime image using maximum reflectance prior
J. Zhang, Y. Cao, S. Fang, Y. Kang, and C. Wen Chen · 2017
Cited alongside, same era.
Curriculum domain adaptation for semantic segmentation of urban scenes
Y. Zhang, P. David, and B. Gong · 2017
Cited alongside, same era.
Pyramid scene parsing network
H. Zhao, J. Shi, X. Qi, X. Wang, and J. Jia · 2017
Cited alongside, same era.
Progressive feature alignment for unsupervised domain adaptation
C. Chen, W. Xie, T. Xu, W. Huang, Y. Rong, X. Ding, Y. Huang, and J. Huang · 2018
Cited alongside, same era.
Encoder-decoder with atrous separable convolution for semantic image segmentation
L.-C. Chen, Y. Zhu, G. Papandreou, F. Schroff, and H. Adam · 2018
Cited alongside, same era.
Cycada: Cycle-consistent adversarial domain adaptation
J. Hoffman, E. Tzeng, T. Park, J.-Y. Zhu, P. Isola, K. Saenko, A. Efros, and T. Darrell · 2018
Cited alongside, same era.
Y.-H. Tsai, W.-C. Hung, S. Schulter, K. Sohn, M.-H. Yang, and M. Chandraker · 2018
Later among the works it cites.
Advent: Adversarial entropy minimization for domain adaptation in semantic segmentation
T.-H. Vu, H. Jain, M. Bucher, M. Cord, and P. Pérez · 2018
Later among the works it cites.
Dcan: Dual channel-wise alignment networks for unsupervised scene adaptation
Z. Wu, X. Han, Y.-L. Lin, M. Gokhan Uzunbas, T. Goldstein, S. Nam Lim, and L. S. Davis · 2018
Later among the works it cites.
Learning semantic representations for unsupervised domain adaptation
S. Xie, Z. Zheng, L. Chen, and C. Chen · 2018
Later among the works it cites.
Fully point-wise convolutional neural network for modeling statistical regularities in natural images
J. Zhang, Y. Cao, Y. Wang, C. Wen, and C. W. Chen · 2018
Later among the works it cites.
Unsupervised domain adaptation for semantic segmentation via class-balanced self-training
Y. Zou, Z. Yu, B. Vijaya Kumar, and J. Wang · 2018
Later among the works it cites.
W. Chang, H. Wang, W. Peng, and W. Chiu · 2019
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Contrastive adaptation network for unsupervised domain adaptation
G. Kang, L. Jiang, Y. Yang, and A. G. Hauptmann · 2019
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Bidirectional learning for domain adaptation of semantic segmentation
Y. Li, L. Yuan, and N. Vasconcelos · 2019
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Famed-net: A fast and accurate multi-scale end-to-end dehazing network
J. Zhang and D. Tao · 2020
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